Accurate identification of driving styles serves as a critical foundation for realizing customized driving experiences, optimizing autonomous driving systems, constructing efficient intelligent traffic management systems, and guiding the design of energy-efficient vehicles. This study aims to achieve efficient, stable, and accurate recognition of diverse driving styles. To this end, an improved driving style recognition method based on the Generalized Regression Neural Network (GRNN) is proposed. This method takes real-time vehicle driving data as the core input, enabling effective classification and precise identification of driving styles. First, the raw collected vehicle driving data were systematically preprocessed to eliminate redundant data and outliers, and three typical driving styles were classified and defined using the classic K-means clustering algorithm. Second, the standardized preprocessed dataset was rationally divided into a training set, an independent test set, and a validation set: the training set was used to optimize and train the GRNN recognition model, while the test set was adopted to quantitatively assess the overall recognition accuracy of the constructed model. Finally, the validation set was employed to comprehensively verify the generalization ability and stability of the model across different driving scenarios. Experimental results demonstrate that the proposed GRNN-based method achieves a driving style recognition accuracy exceeding 96%, with favorable generalization ability and stable performance. The research findings can provide reliable technical support and critical decision-making references for adaptive autonomous driving scenario application, intelligent traffic system optimization, and low-energy-consumption vehicle development.